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base_model: albert/albert-base-v2
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pipeline_tag: text-classification
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<img src="https://dejan.ai/wp-content/uploads/2024/02/dejan.png" alt="Dejan AI Logo" style="pointer-events: none;">
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We build on [the work](https://research.google/pubs/identifying-well-formed-natural-language-questions/) by Manaal Faruqui and Dipanjan Das from [Google AI Language](https://research.google/teams/language/) team to train a search query classifier of well-formed search queries. Our model offers a 10% improvement over Google's classifier by utilising ALBERT architecture instead of LSTM. With accuracy of 80%, the model is production ready and has already been deployed in Dejan AI's query processing pipeline. The role of the model is to help identify query expansion candidates by flagging ambiguous queries retrieved via Google Search Console API.
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base_model: albert/albert-base-v2
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pipeline_tag: text-classification
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<img src="https://dejan.ai/wp-content/uploads/2024/02/dejan.png" alt="Dejan AI Logo" style="pointer-events: none;">
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We build on [the work](https://research.google/pubs/identifying-well-formed-natural-language-questions/) by Manaal Faruqui and Dipanjan Das from [Google AI Language](https://research.google/teams/language/) team to train a search query classifier of well-formed search queries. Our model offers a 10% improvement over Google's classifier by utilising ALBERT architecture instead of LSTM. With accuracy of 80%, the model is production ready and has already been deployed in Dejan AI's query processing pipeline. The role of the model is to help identify query expansion candidates by flagging ambiguous queries retrieved via Google Search Console API.
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